Higher education % in Oregon - Alis volat propriis

Welcome to my notebook covering the use of the tidycensus R package for extracting and evaluating census data in the state of Oregon. We are interested in uncovering the percentages of the population with graduate level degrees at a county level.

Start by importing the list of libraries that will assist us in connecting with the Census Bureau’s APIs and visualizing the desired data.

library(tidycensus) # connect with the census bureau API
library(tidyverse) # data wrangling
library(mapview) # spatial data visualizer
library(plotly) # graph / chart visualizer
library(ggiraph) # graph / chart visualizer
library(ggplot2)
library(DT)
library(scales)
library(sf)

We will use the get_acs() function from tidycensus package to pull data from the census bureau. The function defaults to the 2017-2021 5-year ACS dataset. Which is necessary for us, since there are a list of counties in Oregon with a population of less than 65,000.

# Data objects needed
or_grads <- get_acs(
  geography = "county",
  variables = "DP02_0066P", 
  state = "OR",
  year = 2021,
)

or_grads_table <- or_grads %>%
  separate(NAME, into = c("county", "state"), sep = ", ") %>%
  arrange(-estimate) %>%
  select(county, estimate, moe)

We have our results in a table and we can filter through to see all counties with their percent of population with graduate degrees. Lets use the datatable() function from the DT library to provide a bit more interactivity for previewing the results.

# function from the DT library
datatable(or_grads_table)

Top 3 counties –

---

• Benton
• Multnomah
• Washington

---

Bottom 3 counties –

---

• Malheur
• Gilliam
• Morrow

or_plot <- ggplot(or_grads_table, aes(x = estimate,
                                      y = reorder(county, estimate))) + 
  geom_point(color="navy", size = 3) +
  scale_x_continuous(labels = function(x) paste0(x, '%')) +
  scale_y_discrete(labels = function(x) str_remove(x, " County")) +
  labs(title = "% Population with graduate degrees, 2017-2021 ACS",
       subtitle = "Counties in Oregon",
       caption = "Data acquired with R and tidycensus",
       x = "ACS estimate",
       y = "") +
  theme_minimal(base_size = 12)
  
or_plot

or_plot_errorbar <- ggplot(or_grads, aes(x = estimate, y = reorder(NAME, estimate))) +
  geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe), 
                width = 0.5, linewidth = 0.5) +
  geom_point(color = "gold", size = 3) +
  scale_x_continuous(labels = function(x) paste0(x, '%')) +
  scale_y_discrete(labels = function(x) str_remove(x, " County, Oregon|, Oregon")) +
  labs(title = "% Population with graduate degrees, 2017-2021 ACS",
       subtitle = "Counties in Oregon",
       caption = "Data acquired with R and tidycensus. Error bars represent margin of error around estimates.",
       "ACS estimate",
      y = "",
      x = "Percentage") +
  theme_minimal(base_size = 12)
  
or_plot_errorbar

ggplotly(or_plot_errorbar, tooltip = "x")

NA

# Data objects needed
or_grads_geom <- get_acs(
  geography = "county",
  variables = "DP02_0066P", 
  state = "OR",
  year = 2021,
  geometry = TRUE
)



ggplot() + # use ggplot to create the plot.
    geom_sf(data = or_grads_geom, # use geom_sf to plot the polyline geometry.
            aes(fill = as.factor(estimate)),
            linewidth = 1) + # color based on year.
    scale_color_discrete(name = "estimate") + # set color scale
    coord_sf(datum = NA) + # put geometries on the same coordinate reference system
    theme_void()